Geo-Social Influence Spanning Maximization.

Influence maximization is a recent but well-studied problem which helps identify a small set of users that are most likely to “influence” the maximum number of users in a social network. The problem has attracted a lot of attention as it provides a way to improve marketing, branding, and product adoption. However, existing studies rarely consider the physical locations of the users, but location is an important factor in targeted marketing. In this paper, we propose and investigate the problem of influence maximization in location-aware social networks, or, more generally, Geo-social Influence Spanning Maximization. Given a query $q$<alternatives><inline-graphic xlink:href="li-ieq1-2690288.gif"/></alternatives> composed of a region $R$<alternatives> <inline-graphic xlink:href="li-ieq2-2690288.gif"/></alternatives>, a regional acceptance rate $\rho$<alternatives> <inline-graphic xlink:href="li-ieq3-2690288.gif"/></alternatives>, and an integer $k$<alternatives><inline-graphic xlink:href="li-ieq4-2690288.gif"/></alternatives> as a seed selection budget, our aim is to find the maximum geographic spanning regions (MGSR). We refer to this as the MGSR problem. Our approach differs from previous work as we focus more on identifying the maximum spanning geographical regions within a region $R$<alternatives> <inline-graphic xlink:href="li-ieq5-2690288.gif"/></alternatives>, rather than just the number of activated users in the given network like the traditional influence maximization problem  <xref ref-type="bibr" rid="ref14">[14]</xref> . Our research approach can be effectively used for online marketing campaigns that depend on the physical location of social users. To address the MGSR problem, we first prove NP-Hardness. Next, we present a greedy algorithm with a $1-1/e$<alternatives> <inline-graphic xlink:href="li-ieq6-2690288.gif"/></alternatives> approximation ratio to solve the problem, and further improve the efficiency by developing an upper bounded pruning approach. Then, we propose the OIR -Tree index, which is a hybrid index combining ordered influential node lists with an R -tree. We show that our index based approach is significantly more efficient than the greedy algorithm and the upper bounded pruning algorithm, especially when $k$<alternatives> <inline-graphic xlink:href="li-ieq7-2690288.gif"/></alternatives> is large. Finally, we evaluate the performance for all of the proposed approaches using three real datasets. [ABSTRACT FROM AUTHOR]